A cross-correlation (XCorr) score function is one of the most popular score functions utilized to search peptide identifications in databases, and many computer programs, such as SEQUEST, Comet, and Tide, currently use this score function.
Kim et al BMC Bioinformatics (2018) 19:480 https://doi.org/10.1186/s12859-018-2559-6 SOFTWARE Open Access Accelerating a cross-correlation score function to search modifications using a single GPU Hyunwoo Kim1* , Sunggeun Han2, Jung-Ho Um1 and Kyongseok Park3 Abstract Background: A cross-correlation (XCorr) score function is one of the most popular score functions utilized to search peptide identifications in databases, and many computer programs, such as SEQUEST, Comet, and Tide, currently use this score function Recently, the HiXCorr algorithm was developed to speed up this score function for high-resolution spectra by improving the preprocessing step of the tandem mass spectra However, despite the development of the HiXCorr algorithm, the score function is still slow because candidate peptides increase when post-translational modifications (PTMs) are considered in the search Results: We used a graphics processing unit (GPU) to develop the accelerating score function derived by combining Tide’s XCorr score function and the HiXCorr algorithm Our method is 2.7 and 5.8 times faster than the original Tide and Tide-Hi, respectively, for 50 Da precursor tolerance Our GPU-based method produced identical scores as did the CPU-based Tide and Tide-Hi Conclusion: We propose the accelerating score function to search modifications using a single GPU The software is available at https://github.com/Tide-for-PTM-search/Tide-for-PTM-search Keywords: Peptide identification, Tide, Cross-correlation score function, High performance computing, PTM search Background Peptide identification is one of the most important problems in proteomics Tandem mass spectra (MS/MS) are generated by peptides cleaved from proteins and analyzed using database search methods to identify the peptides [1] An XCorr score function is used by SEQUEST [2], which is the most popular software for peptide identification First, SEQUEST generates theoretical spectra using database sequences, compares the theoretical spectra to an experimental spectrum (called the XCorr score function), and finds the sequence most similar to the experimental spectrum Given that the XCorr score function is time-consuming, this score function was developed to improve performance capabilities Most recently, the HiXCorr algorithm [3] was developed for high-resolution spectra and implemented in conjunction with Tide [4] and * Correspondence: pardess@kisti.re.kr Research Data Hub Center, Korea Institute of Science and Technology Information, Daejeon 34141, Republic of Korea Full list of author information is available at the end of the article Comet [5], with these score function referred to as Tide-Hi and Comet-Hi, respectively However, database search tools using XCorr score functions are still slow because candidate peptides increase when PTMs are considered in the search A multi-thread method exploiting CPU cores has been used to resolve this problem Recently, studies of high-performance computing applications have used GPUs for parallelization Using GPUs, Tempest [6] improved the classical SEQUEST XCorr score function and FastPaSS [7] accelerated the spectral library search method CPUs and GPUs have different methods for data processing The GPU is designed for the simultaneous execution of a single instruction on many threads For this reason, it is a different problem to implement the XCorr score function for each tool using the GPU, though it is an efficient method as a single GPU generally has more cores than a single CPU In this paper, we used the GPU to develop the score function derived by combining Tide’s XCorr score function and the HiXCorr algorithm © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated Kim et al BMC Bioinformatics (2018) 19:480 Implementation Our method is implemented in C++ and NVIDIA’s CUDA (Compute Unified Device Architecture) It appropriately uses both the CPU and the GPU The preprocessing step of the experimental spectra applies the HiXCorr algorithm using the CPU Because the result using HiXCorr algorithm is a sparse vector that increases the time of the dot product step, this result is mapped to a full vector using the GPU (Mapping step) Each thread of the GPU processes a single bin of the full vector in the mapping step After this step, using the CPU, our method extracts candidate peptide sequences (Extracting step); then, using the GPU, our method creates the theoretical spectra (Creating step), and takes the dot product between the experimental spectra and the theoretical spectra (Dot product step) In the creating step and dot product step, each block and each thread of the GPU processes a single candidate peptide and a single peak of the theoretical spectrum, respectively Results For high-resolution spectra analysis, MS data were generated by CPTAC (Clinical Proteomic Tumor Analysis Consortium) Peptide fragmentation was performed with the high-energy collision-induced dissociation (HCD) method The data were acquired on a Thermo Q-Exactive instrument The first fraction of the Com-pRef_Proteome_BI_2 was used; it consists of 33,223 MS/MS data For low-resolution spectra analysis, HAP1 cell was used and peptide fragmentation was collision-induced dissociation (CID) [8] Tandem mass spectra were acquired on a using a linear trap quadrupole (LTQ) Orbitrap Velos mass spectrometer (Thermo Fisher Scientific, Waltham, MA) The first fraction of first replicate (M411-A01-O156-HSP4569–1 and M411-A01-O156-HS-P4569–2) was used; it consists of 25,528 MS/MS data (PreoteomeXchange identifier: PXD006614) The MS/MS data were searched against the SwissProt human-reference (released in July 2016) database Our method is compared with Tide (Crux version 3.1) [9] and Tide-Hi on a machine with an Intel Core i7-7700 K CPU (4.20GHz), 32GB of RAM and an NVIDIA GeForce GTX 1080 8GB GPU under CentOS Tide is generally used with parameters for specific PTMs and, when many PTMs are used, the number of candidate peptides is increased Table shows that the number of candidate peptides is increased when CPTAC data are searched with various PTMs for maximum missed cleavages = 2, number of enzyme termini (NTT) = 2, and precursor tolerance = 0.1 Da (Dalton) Considering many PTMs, Tide is slow because of the increase in the number of candidate peptides Recently, the Open Search method [10] using 500 Da for precursor tolerance has Page of Table Average numbers of candidate peptides for various PTMs using CPTAC data PTM Average number of candidate peptides Non-modified 1089.70 Oxidation (M) 1348.12 Oxidation (M) Deamidation (NQ) 2769.45 Oxidations (M) Deamidations (NQ) 3752.88 Oxidations (M) Deamidations (NQ) Phosphorylations (STY) 8616.11 been proposed for blind search If precursor tolerance = 500 Da, all PTMs for ±500 Da are considered for the database search Actually, the precursor tolerance is the PTMs mass range As such, we changed the precursor tolerance to increase the number of candidate peptides instead of considering PTMs Table shows that as the precursor tolerance increases, the number of candidate peptides increases for maximum missed cleavage = 2, NTT = We compared our method with Tide and Tide-Hi Fragment tolerance = Da was used for low-resolution spectra (HAP1), fragment tolerance = 0.02 Da was used for high-resolution spectra (CPTAC), and the time of tide-search excluding the tide-index time was measured When the number of candidate peptides is small, that is, when the precursor tolerance is narrow, Tide is faster than Tide-Hi for low-resolution spectra (Fig (a), (b)), but Tide-Hi is faster than Tide for high-resolution spectra (Fig (c), (d)), because Tide-Hi is implemented for high-resolution spectra However, as the number of candidate peptides increases, Tide-Hi becomes slower than Tide The time complexity of Tide is O(n) for preprocessing time and O (mPt) for calculated time of XCorr, where n is the size of the spectrum bin for the fragment tolerance, m is the number of candidate peptides, and Pt is the number of peaks in each theoretical spectrum On Table Average numbers of candidate peptides for various precursor tolerances using CPTAC data Precursor tolerance Average number of candidate peptides 0.1 1089.70 0.2 1537.49 0.5 1641.89 3275.70 6547.26 16,332.48 10 32,556.57 20 65,217.44 50 162,928.67 Kim et al BMC Bioinformatics (2018) 19:480 Page of a b c d Fig Comparison of total running time for Tide, Tide-Hi, and our method when 8-threads and various precursor tolerances were used a, b Running time for low resolution spectra (fragment tolerance = Da) c, d Running time for high-resolution spectra (fragment tolerance = 0.02 Da) b and d show enlarged results up to precursor tolerance = Da in (a) and (b), respectively a b c d Fig Comparison of total running time for Tide, Tide-Hi, and our method when various threads and low-resolution spectra (fragment tolerance = Da) were used a Single thread b 2-threads c 4-threads d 8-threads Kim et al BMC Bioinformatics (2018) 19:480 Page of a b c d Fig Comparison of total running time for Tide, Tide-Hi, and our method when various threads and high-resolution spectra (fragment tolerance = 0.02 Da) were used a Single thread b 2-threads c 4-threads d 8-threads the other hand, the time complexity of Tide-Hi is O (Pe) for preprocessing time and O(m (Pe + Pt)) for calculated time of XCorr, where Pe is the number of peaks in the experimental spectrum If m (the number of candidate peptides) increases, O (mPe) becomes larger than O(n), so that Tide-Hi becomes slower than Tide For this reason, Tide-Hi is slower than Tide as the number of candidate peptide increases Our method, utilizing a single GPU, uses the HiXCorr algorithm to speed up the search for high-resolution spectra even as the number of candidate peptides increases Figure shows that the proposed method is faster than Tide-Hi and Tide even as the number of candidate peptides increases Our method is faster than Tide and Tide-Hi regardless of the number of candidate peptides, or the resolution of the spectra For low- and high-resolution spectra, our method is 2.7 and 5.8 times faster than Tide and Tide-Hi at a 50 Da precursor tolerance Since Tide uses the multi-thread method, we measured the times by changing the number of threads Figures and show that when using low- and high-resolution spectra, our method is faster than Tide and Tide-Hi, respectively, regardless of the number of threads Our GPU-based method produced identical scores as did the CPU-based Tide and Tide-Hi Conclusions We propose an accelerating score function to search modifications using a single GPU We used the GPU to develop the accelerating score function, which was derived by combining Tide’s XCorr score function and the HiXCorr algorithm For low- and high-resolution spectra, our method is 2.7 and 5.8 times faster than the Tide and Tide-Hi for 50 Da precursor tolerance The software is available at https://github.com/Tide-for-PTM-search/ Tide-for-PTM-search Availability and Requirements Project name: Tide for PTM search Project home page: https://github.com/Tide-forPTM-search/Tide-for-PTM-search Operating system(s): CentOS Programming language: C++, CUDA License: Apache license Any restrictions to use by non-academics: none Example data: available at project homepage Abbreviations CPTAC: Clinical proteomic tumor analysis consortium; CUDA: Compute unified device architecture; Da: Dalton; GPU: Graphics processing unit; MS/ MS: Tandem mass spectra; NTT: Number of enzyme termini; PTM: Posttranslational modification; XCorr: Cross-correlation Kim et al BMC Bioinformatics (2018) 19:480 Acknowledgments Not applicable Funding This research was supported by Korea Institute of Science and Technology Information (KISTI) which played roles in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript Availability of data and materials Software and dataset (MS/MS data and a database) are available at https:// github.com/Tide-for-PTM-search/Tide-for-PTM-search Authors’ contributions HK conceived the project and designed the studies HK, SH, JU, and KP performed the analysis and wrote the manuscript All authors read and approved the final manuscript Ethics approval and consent to participate Not applicable Consent for publication Not applicable Competing interests The author declare that they have no competing interests Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations Author details Research Data Hub Center, Korea Institute of Science and Technology Information, Daejeon 34141, Republic of Korea 2KISTI Scientific Data School, Korea Institute of Science and Technology Information, Daejeon 34141, Republic of Korea 3Super Computing Cloud Center, Korea Institute of Science and Technology Information, Daejeon 34141, Republic of Korea Received: 28 February 2018 Accepted: December 2018 References Steen H, Matthias M The ABC's (and XYZ's) of peptide sequencing Nat Rev Mol Cell Biol 2004;5(9):699–711 Eng JK, Ashley LM, John RY An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database J Am Soc Mass Spectrom 1994;5(11):976–89 Kim H, Jo H, Park H, Paek E HiXCorr: a portable high-speed XCorr engine for high-resolution tandem mass spectrometry Bioinformatics 2015;31(24): 4026–8 Diament BJ, Noble WS Faster SEQUEST searching for peptide identification from tandem mass spectra J Proteome Res 2011;10(9):3871–9 Eng JK, Jahan TA, Comet HMR An open-source MS/MS sequence database search tool Proteomics 2015;13(1):22–4 Milloy JA, Faherty BK, Gerber SA Tempest: GPU-CPU computing for highthroughput database spectral matching J Proteome Res 2012;11(7):3581–91 Baumgardner LA, Shanmugam AK, Lam H, Eng JK, Martin DB Fast parallel tandem mass spectral library searching using GPU hardware acceleration J Proteome Res 2011;10(6):2882–8 Lee SE, Song J, Bösl K, Müller AC, Vitko D, Bennett KL, Superti-Furga G, Pandey A, Kandasamy RK, Kim MS Proteogenomic analysis to identify missing proteins from haploid cell lines Proteomics 2018;18(8):1700386 McIlwain S, Tamura K, Kertesz-Farkas A, Grant CE, Diament B, Frewen B, Howbert JJ, Hoopmann MR, kall L, Eng JK, MacCoss MJ, Noble WS Crux: rapid open source protein tandem mass spectrometry analysis J Proteome Res 2014;13(10):4488–91 10 Chick JM, Kolippakkam D, Nusinow DP, Zhai B, Rad R, Huttlin EL, Gygi SP A mass-tolerant database search identifies a large proportion of unassigned spectra in shotgun proteomics as modified peptides Nat Biotechnol 2015; 33(7):743–9 Page of ... propose an accelerating score function to search modifications using a single GPU We used the GPU to develop the accelerating score function, which was derived by combining Tide’s XCorr score function. .. system(s): CentOS Programming language: C++, CUDA License: Apache license Any restrictions to use by non-academics: none Example data: available at project homepage Abbreviations CPTAC: Clinical proteomic... database search Actually, the precursor tolerance is the PTMs mass range As such, we changed the precursor tolerance to increase the number of candidate peptides instead of considering PTMs Table